Adversarial Risk Analysis (ARA) builds on statistical risk analysis and game theory to analyze decision situations involving two or more intelligent opponents who make decisions under uncertainty. During the past few years, the ARA approach-which is based on the explicit modelling of the decision making processes of a rational opponent-has been applied extensively in areas such as counterterrorism and corporate competition. In the context of military combat modelling, however, ARA has not been used systematically, even if there have been attempts to predict the opponent’s decisions based on wargaming, application of game theoretic equilibria, and the use of expert judgements. Against this backdrop, we argue that combining ARA with military combat modelling holds promise for enhancing the capabilities of combat modelling tools. We identify ways of combining ARA with combat modelling and give an illustrative example of how ARA can provide insights into a problem where the defender needs to estimate the utility gained from hiding its troop movements from the attacker. Even if the ARA approach can be challenging to apply, it can be instructive in that relevant assumptions about the resources, expectations and goals that guide the adversary’s decisions must be explicated.
Banks, D., Petralia, F., & Wang, S. (2011). Adversarial risk analysis: Borel games. Applied Stochastic Models in Business and Industry 27(2), pp. 72-86.
Brown, G. & Washburn, A.R. (2000 rev. 2004). The fast theater model (FATHM), Project Report, (NPS-OR-01-002-PR), Naval Postgraduate School, Monterey, CA.
Camerer, C. (2003). Behavioral Game Ttheory: Experiments in Strategic Interaction. Princeton University Press, New Jersey.
Caswell, D. J., Howard, R. A., & Paté-Cornell, M. E. (2011). Analysis of national strategies to counter a country's nuclear weapons program. Decision Analysis, 8(1), 30-45.
Churchill, D., Saffidine, A., & Buro, M. (2012). Fast Heuristic Search for RTS Game Combat Scenarios. In AIIDE.
Davis, P. K., & Blumenthal, D. (1991). The base of sand problem: A white paper on the state of military combat modeling (No. RAND/N-3148-OSD/DARPA). Defense Advanced Research Projects Agency, Arlington VA.
Golany, B., Kaplan, E. H., Marmur, A., & Rothblum, U. G. (2009). Nature plays with dice-terrorists do not: Allocating resources to counter strategic versus probabilistic risks. European Journal of Operational Research, 192(1), 198-208.
Howard, R. A. & Matheson, J. E. (2005). Influence diagrams. Decision Analysis, 2(3), pp. 127-143.
Kadane, J. B. & Larkey, P. D. (1982). Subjective probability and the theory of games. Management Science, 28(2), pp. 113-120.
Kangas, L. (2005). Taistelun stokastinen mallinnus. Master's Thesis, Helsinki University of Technology. http://sal.aalto.fi/publications/pdf-files/tkan05.pdf Accessed: 2014-05-07
Kangas, L. & Lappi, E. (2006) Probabilistic risk analysis in combat modeling. In: Hämäläinen, J. (ed.) Lanchester and Beyond. A Workshop on Operational Analysis Methodology. PVTT Publications 11
Kangaspunta, J., Liesiö, J., & Salo, A. (2012). Cost-efficiency analysis of weapon system portfolios. European Journal of Operational Research, 223(1), pp. 264-275.
Kardes, E., & Hall, R. (2005). Survey of literature on strategic decision making in the presence of adversaries. Unpublished report.
Kovenock, D., Roberson, B. (2010). Conflicts with multiple battlefields, CESifo working paper: Empirical and Theoretical Methods, No. 3165
Kroshl, W. M., Sarkani, S., & Mazzuchi, T. A. (2015). Efficient allocation of resources for defense of spatially distributed networks using agent‐based simulation. Risk Analysis (to appear).
Lappi, E. (2012). Computational methods for tactical simulations. Julkaisusarja 1. N:o 1/2012. Doctoral Thesis, National Defence University, Finland
Lappi, E., Pakkanen, M., & Åkesson, B. (2012). An approximative method of simulating a duel. In: Proceedings of the Winter Simulation Conference, WSC '12, pp. 208:1-208:10
Lappi, E., Pentti, J., Åkesson, B., Roponen, J., Valtonen, J., Koskinen, J., Burhan, U., Sivertun, Å., and Hämäläinen, J. (2015). Team 4: Data farm. manuscript.
Meng, S., Wiens, M., & Schultmann, F. (2014). A game-theoretic approach to assess adversarial risks. In: Brebbia, C.A. (ed.), Risk Analysis IX, WIT Press, Southampton, UK, pp. 141-152.
Myerson, R. B. (1991). Game Theory: Analysis of Conflict. Harvard University Press, Cambridge, MA.
Paté-Cornell, E., & Guikema, S. (2002). Probabilistic modeling of terrorist threats: A systems analysis approach to setting priorities among countermeasures. Military Operations Research, 7(4), pp. 5-23.
Reese, W. (1980). Deception in a game theoretic framework. In: Daniel, D. C., Herbig, K. L., Reese, W., Heuer, R. J., & Sarbin, T. R. (1980). Multidisciplinary Perspectives on Military Deception (No. NPS-56-80-012A). Naval Postgraduate School, Monterey, CA.
Rios Insua, D., Rios, J., & Banks, D. (2009). Adversarial risk analysis. Journal of the American Statistical Association, 104(486), pp. 841-854.
Rios, J., & Rios Insua, D. (2012). Adversarial risk analysis for counterterrorism modeling. Risk analysis, 32(5), pp. 894-915.